Simulation on water and sand separation from crude oil in settling tanks based on the particle model

A generalized shaping filter method for higher order statistics

In this paper a new method is presented for the formulation and solution of two-time, response-excitation moment equations for a nonlinear dynamical system excited by colored, Gaussian or non-Gaussian processes. Starting from equations for the two-time moments (e.g. for Cxy(t,s), Cxx(t,s)), the method uses an exact time-closure condition, in addition to a Gaussian moment closure, in order to obtain a closed, non-local in time (causal) subsystem for the one-time (t=s) moments. After solving this causal system, the two-time moments can be calculated for all (t,s) pairs as well. The present method differs essentially from the classical It\^o/FPK approach since it does not involve any specific assumptions regarding the correlation structure of the excitation. In the case where the input random process can be obtained as the solution of an It\^o equation (as, e.g., happens with an Ornstein-Uhlenbeck process), the proposed non-local system is localized, leading to moment equations identical with the usual ones. The closed, non-local in time, moment system is numerically solved by means of an appropriate, two-scale, iterative scheme, and numerical results are presented for two families of colored stochastic excitations. The results are confirmed by means of extensive Monte Carlo simulations. It is found that both the correlation time and the details of the shape of the input random function affect appreciable the response covariance. In the present paper we focus on a monostable cubic half-oscillator, excited by a smoothly-correlated, linear-plus-cubic-Gaussian (non-Gaussian) random input. The bistable case, as well as more general nonlinear systems can be treated by the same method, provided that a more elaborate moment closure will be used.

Two-time, response-excitation moment equations for a cubic half-oscillator under Gaussian and cubic-Gaussian colored excitation. Part 1: The monostable case

A novel and effective approach to the global motion estimation and moving object extraction is proposed in this paper. Firstly, the translational motion model was used by the fact that complex motion can be decomposed as a sum of translational components. Then in this application, the edge gray horizontal and vertical projections were used as the block matching feature for the motion vector estimation. The proposed algorithm reduces the motion estimation computations by calculating the one-dimensional vector rather than the two-dimensional ones. Once the global motion is robustly estimated, relatively stationary background can be almost completely eliminated through inter-frame difference method. To achieve an accurate object extraction result, the higher-order statistics (HOS) algorithm was used to discriminate background and moving object. Experiments have shown a robust result for global motion estimation and object extraction.

Moving target detection in image sequences

A framework is introduced to develop the theory of the adjoint method for models including both continuous and discrete dynamics. The basis of this framework consists of the class of impulsive linear dynamic systems. It allows extension of the adjoint method to more general models that include multirate subsystems or nonperiodic discrete events. Both the deterministic and the stochastic case are considered and the theory is applied to study the effect of uplinks on the performance of a guidance system.

Adjoint Method for Hybrid Guidance Loop State-Space Models

An extension of the Adjoint Method is presented to evaluate higher order statistics of the miss distance. This extension allows to predict more accurately the effect of nonGaussian perturbations on the accuracy of the guidance loop. The theory is illustrated on the classical example of a target performing a constant step lateral manoeuvre with random start time. It is shown that the method presented in this paper is able to predict the higher order moments of the miss distance. Using these higher order moments, it is possible to obtain an approximation of the miss distance distribution and ultimately to evaluate the probability of hit without Monte Carlo simulations.

Higher Order Statistics and the Adjoint Method for guidance loop analysis

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A generalized shaping filter method for higher order statistics

Higher Order Statistics and the Adjoint Method for guidance loop analysis